Identification of Key Drivers of Net Promoter Score Using a Statistical Classification Model - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 145

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Identification of Key Drivers of Net Promoter
Score Using a Statistical Classification Model
Daniel R. Jeske 1 , Terrance P. Callanan 2 and Li Guo 3
1 Professor and Chair, Department of Statistics,
University of California, Riverside, CA
2 Customer Experience Manager, Six Sigma Champion,
Carestream Health, Rochester, NY
3 Graduate Student, Department of Statistics,
University of California, Riverside, CA
USA
1. Introduction
Net Promoter Score (NPS) is a popular metric used in a variety of industries for measuring
customer advocacy. Introduced by Reichheld (2003), NPS measures the likelihood that an
existing customer will recommend a company to another prospective customer. NPS is
derived from a single question that may be included as part of a larger customer survey. The
single question asks the customer to use a scale of 0 to 10 to rate their willingness and
intention to recommend the company to another person. Ratings of 9 and 10 are used to
characterize so-called 'promoters,' ratings of 0 through 6 characterize 'detractors,' and
ratings of 7 and 8 characterize 'passives.' The NPS is calculated as the percentage of
respondents that are promoters minus the percentage of respondents that are detractors.
The idea behind the labels given to customers is as follows. Promoters are thought to be
extremely satisfied customers that see little to no room for improvement, and consequently
would offer persuasive recommendations that could lead to new revenue. The passive
ratings, on the other hand, begin to hint at room for improvement and consequently the
effectiveness of a recommendation from a Passive may be muted by explicit or implied
caveats. Ratings at the low end are thought to be associated with negative experiences that
might cloud a recommendation and likely scare off prospective new customers. Additional
discussion on the long history of NPS can be found in Hayes (2008).
Some implementations of NPS methodology use reduced 5-point or 7-point scales that align
with traditional Likert scales. However it is implemented, the hope is that movements in
NPS are positively correlated with revenue growth for the company. While Reichheld's
research presented some evidence of that, other findings are not as corroborative
(Kenningham et al., 2007). Regardless of whether there is a predictive relationship between
NPS and revenue growth, implementing policies and programs within a company that
improve NPS is an intuitively sensible thing to do [see, for example, Vavra (1997)]. A
difficult and important question, however, is how to identify key drivers of NPS. Calculating
NPS alone does not do this.